背景:具有清晰组织病理学切缘的手术切除是预防恶性扁豆(LM)进展为侵袭性黑色素瘤的首选治疗方法。然而,对太阳损伤皮肤切除边缘的评估具有挑战性.我们开发了一种深度学习模型,用于检测LM切除边缘的黑素细胞。
方法:总共,包括353个完整的幻灯片图像(WSI)。295个WSI用于培训,58个WSI用于验证和测试。该算法使用3,973个手动逐像素注释进行训练。将AI分析与三名失明的皮肤病理学家和两名病理学居民的分析进行了比较,他们在没有人工智能和人工智能辅助的情况下进行了评估。免疫组织化学(SOX10)用作参考标准。我们对低复发风险和高复发风险使用二分法(≤25个黑素细胞在0.5mm的区域为低风险,>25个为高风险)。
结果:AI模型在区分具有低和高复发风险的边缘方面实现了0.84的受试者工作特征曲线(AUC)下面积。相比之下,皮肤病理学家的AUC范围从0.72到0.90,而病理学的居民,0.68至0.80。此外,借助AI模型,两名病理学家的表现显着提高。
结论:深度学习在检测具有高复发风险和低复发风险的LM切除边缘方面显示出显著的准确性。此外,人工智能的使用提高了2/5病理学家的表现。这种自动化工具可以帮助病理学家评估或预筛查LM边缘。
BACKGROUND: Surgical excision with clear histopathological margins is the preferred treatment to prevent progression of lentigo maligna (LM) to invasive melanoma. However, the assessment of resection margins on sun-damaged skin is challenging. We developed a deep learning model for detection of melanocytes in resection margins of LM.
METHODS: In total, 353 whole slide images (WSIs) were included. 295 WSIs were used for training and 58 for validation and testing. The algorithm was trained with 3,973 manual pixel-wise annotations. The AI analyses were compared to those of three blinded dermatopathologists and two pathology residents, who performed their evaluations without AI and AI-assisted. Immunohistochemistry (SOX10) served as the reference standard. We used a dichotomized cutoff for low and high risk of recurrence (≤ 25 melanocytes in an area of 0.5 mm for low risk and > 25 for high risk).
RESULTS: The AI model achieved an area under the receiver operating characteristic curve (AUC) of 0.84 in discriminating margins with low and high recurrence risk. In comparison, the AUC for dermatopathologists ranged from 0.72 to 0.90 and for the residents in pathology, 0.68 to 0.80. Additionally, with aid of the AI model the performance of two pathologists significantly improved.
CONCLUSIONS: The deep learning showed notable accuracy in detecting resection margins of LM with a high versus low risk of recurrence. Furthermore, the use of AI improved the performance of 2/5 pathologists. This automated tool could aid pathologists in the assessment or pre-screening of LM margins.